{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import einsum\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.8862745098039215, 0.2901960784313726, 0.2],\n",
       " [0.47843137254901963, 0.40784313725490196, 0.6509803921568628],\n",
       " [0.20392156862745098, 0.5411764705882353, 0.7411764705882353],\n",
       " [0.09411764705882353, 0.5176470588235295, 0.5294117647058824],\n",
       " [0.6509803921568628, 0.023529411764705882, 0.1568627450980392],\n",
       " [0.27450980392156865, 0.47058823529411764, 0.12941176470588237]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot_colors = [\n",
    "    \"E24A33\",\n",
    "    \"7A68A6\",\n",
    "    \"348ABD\",\n",
    "    \"188487\",\n",
    "    \"A60628\",\n",
    "    \"467821\"\n",
    "]\n",
    "ggplot_colors_rgb = [\n",
    "    [int(c[2*i:2*i+2],base=16)/255 for i in range(3)]\n",
    "    for c in ggplot_colors\n",
    "]\n",
    "ggplot_colors_rgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"440\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e24a33;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#995f84;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5c77b0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3089b6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1c858e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#693c51;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8a2826;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#467821;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.8862745098039215, 0.2901960784313726, 0.2),\n",
       " (0.5983852364475202, 0.3732410611303345, 0.5183391003460208),\n",
       " (0.3600153787004998, 0.465359477124183, 0.6898885044213764),\n",
       " (0.18885044213763938, 0.5379469434832757, 0.7121107266435986),\n",
       " (0.1091887735486352, 0.5208765859284891, 0.5584775086505191),\n",
       " (0.4107650903498653, 0.2366782006920417, 0.31757016532103044),\n",
       " (0.5402537485582468, 0.15501730103806227, 0.14878892733564014),\n",
       " (0.27450980392156865, 0.47058823529411764, 0.12941176470588237)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cmap = sns.blend_palette(ggplot_colors_rgb,n_colors=8)\n",
    "my_cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 50\n",
    "coef = 10*np.random.randn(8)\n",
    "x = np.linspace(-1,1,num)\n",
    "fig,ax = plt.subplots()\n",
    "data = einsum(\"i,j->ij\",coef,x)\n",
    "for d,c in zip(data,my_cmap):\n",
    "    ax.plot(x,d,color=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x199fbad9d00>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(x,x,color=my_cmap[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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    "np.einsum(\"i,j->ij\",coef,x)"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1,1,100)\n",
    "plt.plot(x,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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